A look at Image Recognition...

Over a relatively small period of time - mere years - automated image recognition has moved on in massive leaps and bounds.

Computer Vision is all the rage

Over a relatively small period of time - mere years - automated image recognition has moved on in massive leaps and bounds.

Only a matter of years ago, should you want to process a large number of images and have a computer tell you what is in each one, you’d have to set up your own Linux box with Python with various image processing libraries such as OpenCV (an open source computer vision library that’s been going since 2000! Amazing!) and get familiar with deep learning techniques in order to train and constantly improve the image classification for your image recognition model.

Although not impossible, this is not something for the fainthearted. If you decide to set your own system up, please let us know - it would be a fascinating write-up, no doubt.

It’s APIs all the way down

Thanks to the advent of cloud computing, various big players such as Amazon, Microsoft, and Google have developed their own image recognition services for you to use. They’re doing all the heavy lifting and setting up that deep learning image classification back-end, then exposing this service for us muggles to play with over the simplest of endpoints.

Skynet is coming.

Why not have a try? You can get free trials with Microsoft Azure’s Cognitive Services and have a play with the Computer Vision solution until you’re convinced it’s both 1) awesome and 2) easy.

What could you build? I’ve seen a fantastic hack using Amazon’s Rekognition to process a photo from a RaspberryPi (which was placed in an un-bookable meeting room with no windows) to regularly check if a person can be seen, and turn on a “busy” light if so to save people constantly opening the door to check. Brilliant!

Amazing, but the possibilities are scary. Where the heck is Kyle Reese when you need him?*